Articles in category: Big Data and Analytics

I believe it was Sartre who wisely said hell is conversational AI. Despite the best intentions of engineers, today’s machine learning really is the savior and handicap of personal assistants. Berkeley-based startup Semantic Machines might suffer the same Achilles’ heel, but its team of 18 artificial intelligence PhDs thinks it can get farther than the current state-of-the-art establishment.

We tend to think of machines, in particular smart machines, as somehow cold, calculating and unbiased. We believe that self-driving cars will have no preference during life or death decisions between the driver and a random pedestrian. And we understand that learning systems will always converge on ground truth because unbiased algorithms drive them. For some of us, this is a bug: Machines should not be empathetic outside of their rigid point of view. For others, it is a feature: They should be freed of human bias. But in the middle, there is the view they will be objective.

For developing client-side user interfaces for web applications, HTML, JavaScript and CSS constitute a potent triumvirate for showing content (HTML and possibly XML), input validation and programming functionality (JavaScript), as well as formatting and layout (CSS). Features exclusive to HTML5 and soon-to-arrive 5.1 give even more power to web developers, including better support for graphics and media, which will be critical as the battle for consumers' eyeballs intensifies.

Deep Learning is not only a massive buzzword spanning business and technology but also a concept that will transform most industries and jobs, as well as the way we live our lives. However, there is confusion about what it is and how it differs from Machine Learning and Artificial Intelligence (AI). Over the past few years, the term “deep learning” has firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics.

Your business spends a lot of time analyzing the market you sell to, but can you take a step back and instead create the market that you want? Experts in the little-known field of behavioral economics and market design say “yes.”

Multi-Level Security Information Systems, better known as MLS systems, have proven merit in the DoD arena in terms of providing a security net and thwarting threats to data and infrastructure within a unified system. Certainly this type of implementation would make sense commercially, but in the fast moving, ever-changing enterprise space, CTOs have historically been hesitant to adopt some of the advancements in these trusted operating systems on top of optimized hardware.

Work is becoming increasingly automated thanks to the advancement of technology like machine learning and artificial intelligence, potentially to the detriment of laborers. But not all service providers are being immediately affected by the robot revolution, says Thumbtack’s economist Lucas Puente. Thumbtack, which connects service providers with customers, says the company’s data shows people who list their services on Thumbtack have seen little negative impact.

Artificial intelligence has fascinated mankind for more than half a century, with the first public mention of computer intelligence recorded during a London lecture by Alan Turing in 1947. More recently, the public has been exposed to headlines that have increasingly contained references to the growing power of AI, whether that’s been AlphaGo’s defeat of legendary Go player Lee Se-dol, Microsoft’s racist AI bot named Tay or any other number of new developments in the machine learning field. Once a plot device for science-fiction tales, AI is becoming real — and human beings are going to have to ...

Real-time marketing techniques, personalization, the use of contextual clues, and the rapid convergence of marketing technology (Martech) and advertising technology (Adtech) are four key forces driving the future of data-centric marketing. The 2016 Hype Cycle for Digital Marketing and Advertising reflects how the combined effect of these four forces are fueling innovative new uses of analytics, contextual clues enabled by Internet of Things (IoT) devices, machine learning, personalization and a more data-centric approach to driving marketing strategies.

As Saudi Arabia gears up to diversify its economy away from oil, local businesses are turning to big data analytics. The Saudi Vision 2030, announced in August, aims to increase non-oil revenue sixfold from $43.5bn to $267bn a year by liberalising business policies and introducing entrepreneurship initiatives. This turning point in Saudi Arabia’s economic history is driving IT investment to record levels. According to IDC, tech spending in the kingdom is set to pass $35bn in 2016 as organisations embrace digital transformation initiatives to optimise costs and improve business process efficiencies.

Growing numbers of organizations are augmenting their enterprise data warehouses (EDWs) with the open source Apache Hadoop platform, and gaining the associated benefits — from cost economies to a highly scalable repository for data in its many forms. Now the focus is shifting to the steps an organization can take to make Hadoop a true enterprise-class platform.

Researchers are testing ways for robots to perform complex tasks by downloading what others have learned. In three separate research papers posted online Monday, researchers at Google and other Alphabet subsidiaries showed several ways in which robots can learn to perform simple tasks more quickly by sharing different types of learning experiences.

Hard on the heels of the discovery of the largest known data breach in history, Cloudera and Intel on Wednesday announced that they've donated a new open-source project to the Apache Software Foundation with a focus on using big data analytics and machine learning for cybersecurity. Originally created by Intel and launched as the Open Network Insight (ONI) project in February, the effort is now called Apache Spot and has been accepted into the ASF Incubator.

Big data is in many ways still a wild frontier, requiring wily smarts and road-tested persistence on the part of those hoping to find insight in all the petabytes. On Tuesday, IBM announced a new platform it hopes will make things easier.Dubbed Project DataWorks, the new cloud-based platform is the first to integrate all types of data and bring AI to the table for analytics, IBM said.

68% of manufacturers are currently investing in data analytics. 46% of manufacturers agree that implementing and using data analytics is no longer optional. 32% see the potential for big data analytics and Industrial Internet of Things (IIoT) to improve supply chain performance and increase revenue.

ProductBoard today is launching a service for digital product managers that helps them better organize user research and determine which features deserve priority. Making its public debut at TechCrunch Disrupt San Francisco, this product management platform is already being used by over 100 paying customers during its beta to establish their product roadmaps and collaborate with engineering teams on their upcoming feature releases.

There is a new generation of companies exerting such great influence on society that they’re essentially becoming utilities. Google, Amazon, Facebook and Uber are near monopolies that provide services as integral to our modern life as power, telephone and transportation systems were a century ago. But there is a key difference between last century’s utilities and today’s…

IBM today announced the launch of the aptly-named IBM Power Systems S822LC for High Performance Computing. Its unwieldy name betrays the fact that this is a really interesting product. Together with Nvidia, IBM built this new system specifically for artificial intelligence, machine learning and advanced analytics use cases. The new server uses two of IBM’s POWER8 CPUs and four of…

Big data has been a big buzzword for more than a few years already, and it's got some solid numbers to back that up, including US$46 billion in 2016 revenues for vendors of related products and services. But the big data era is still just beginning to dawn, with the real growth yet to come.

For organizations to stay above the rising tide of data agility, they’ll need to innovate their data storage options through next-generation databases. Subsequently, they benefit from greater scalability and performance, with lower cost of ownership.

With the advent of natural language processing (NLP) technologies and the ability to understand more unstructured data, like phone call recordings, companies are sitting on a wealth of information every time they record a call. And because the field is expanding so rapidly, there are many different ways companies can put this data to use.

Much has been made about the business implications of recent, rapid advancements in cognitive computing — that is, the possibility of advanced analytics tools to help human knowledge workers glean actionable insight from vast and deep lakes of historical, transactional and machine-generated information.